| 1. |
- Armesto, N., et al.
(författare)
-
Heavy-ion collisions at the LHC-Last call for predictions
- 2008
-
Ingår i: Journal of Physics G. - : IOP Publishing. - 0954-3899 .- 1361-6471. ; 35:5, s. 054001-
-
Forskningsöversikt (refereegranskat)abstract
- This writeup is a compilation of the predictions for the forthcoming Heavy Ion Program at the Large Hadron Collider, as presented at the CERN Theory Institute 'Heavy Ion Collisions at the LHC - Last Call for Predictions', held from 14th May to 10th June 2007.
|
|
| 2. |
- Meech, K. J., et al.
(författare)
-
EPOXI: Comet 103P/Hartley 2 Observations from a Worldwide Campaign
- 2011
-
Ingår i: Astrophysical Journal Letters. - London : IOP. - 2041-8213 .- 2041-8205. ; 734:L1, s. 1-9
-
Tidskriftsartikel (refereegranskat)abstract
- Earth- and space-based observations provide synergistic information for space mission encounters by providing data over longer timescales, at different wavelengths and using techniques that are impossible with an in situ flyby. We report here such observations in support of the EPOXI spacecraft flyby of comet 103P/Hartley 2. The nucleus is small and dark, and exhibited a very rapidly changing rotation period. Prior to the onset of activity, the period was ~16.4?hr. Starting in 2010 August the period changed from 16.6?hr to near 19?hr in December. With respect to dust composition, most volatiles and carbon and nitrogen isotope ratios, the comet is similar to other Jupiter-family comets. What is unusual is the dominance of CO 2 -driven activity near perihelion, which likely persists out to aphelion. Near perihelion the comet nucleus was surrounded by a large halo of water-ice grains that contributed significantly to the total water production.
|
|
| 3. |
- Degas, A., et al.
(författare)
-
A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management : Current Trends and Development with Future Research Trajectory
- 2022
-
Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:3
-
Forskningsöversikt (refereegranskat)abstract
- Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
|
|
| 4. |
- Hurter, C., et al.
(författare)
-
Usage of more transparent and explainable conflict resolution algorithm : Air traffic controller feedback
- 2022
-
Ingår i: Transportation Research Procedia. - : Elsevier B.V.. - 2352-1457 .- 2352-1465. ; 66:C, s. 270-278
-
Tidskriftsartikel (refereegranskat)abstract
- Recently, Artificial intelligence (AI) algorithms have received increasable interest in various application domains including in Air Transportation Management (ATM). Different AI in particular Machine Learning (ML) algorithms are used to provide decision support in autonomous decision-making tasks in the ATM domain e.g., predicting air transportation traffic and optimizing traffic flows. However, most of the time these automated systems are not accepted or trusted by the intended users as the decisions provided by AI are often opaque, non-intuitive and not understandable by human operators. Safety is the major pillar to air traffic management, and no black box process can be inserted in a decision-making process when human life is involved. To address this challenge related to transparency of the automated system in the ATM domain, we investigated AI methods in predicting air transportation traffic conflict and optimizing traffic flows based on the domain of Explainable Artificial Intelligence (XAI). Here, AI models’ explainability in terms of understanding a decision i.e., post hoc interpretability and understanding how the model works i.e., transparency can be provided for air traffic controllers. In this paper, we report our research directions and our findings to support better decision making with AI algorithms with extended transparency.
|
|
| 5. |
- Wynberg, Elke, et al.
(författare)
-
Variability in white blood cell count during uncomplicated malaria and implications for parasite density estimation : a WorldWide Antimalarial Resistance Network individual patient data meta-analysis
- 2023
-
Ingår i: Malaria Journal. - : Springer Nature. - 1475-2875. ; 22
-
Tidskriftsartikel (refereegranskat)abstract
- Background: The World Health Organization (WHO) recommends that when peripheral malarial parasitaemia is quantified by thick film microscopy, an actual white blood cell (WBC) count from a concurrently collected blood sample is used in calculations. However, in resource-limited settings an assumed WBC count is often used instead. The aim of this study was to describe the variability in WBC count during acute uncomplicated malaria, and estimate the impact of using an assumed value of WBC on estimates of parasite density and clearance.Methods: Uncomplicated malaria drug efficacy studies that measured WBC count were selected from the WorldWide Antimalarial Resistance Network data repository for an individual patient data meta-analysis of WBC counts. Regression models with random intercepts for study-site were used to assess WBC count variability at presentation and during follow-up. Inflation factors for parasitaemia density, and clearance estimates were calculated for methods using assumed WBC counts (8000 cells/mu L and age-stratified values) using estimates derived from the measured WBC value as reference.Results: Eighty-four studies enrolling 27,656 patients with clinically uncomplicated malaria were included. Geometric mean WBC counts (x 1000 cells/mu L) in age groups < 1, 1-4, 5-14 and >= 15 years were 10.5, 8.3, 7.1, 5.7 and 7.5, 7.0, 6.5, 6.0 for individuals with falciparum (n = 24,978) and vivax (n = 2678) malaria, respectively. At presentation, higher WBC counts were seen among patients with higher parasitaemia, severe anaemia and, for individuals with vivax malaria, in regions with shorter regional relapse periodicity. Among falciparum malaria patients, using an assumed WBC count of 8000 cells/mu L resulted in parasite density underestimation by a median (IQR) of 26% (4-41%) in infants < 1 year old but an overestimation by 50% (16-91%) in adults aged = 15 years. Use of age-stratified assumed WBC values removed systematic bias but did not improve precision of parasitaemia estimation. Imprecision of parasite clearance estimates was only affected by the within-patient WBC variability over time, and remained < 10% for 79% of patients.Conclusions: Using an assumed WBC value for parasite density estimation from a thick smear may lead to underdiagnosis of hyperparasitaemia and could adversely affect clinical management; but does not result in clinically consequential inaccuracies in the estimation of the prevalence of prolonged parasite clearance and artemisinin resistance.
|
|
